A Semantic Path-Based Similarity Measure for Weighted Heterogeneous Information Networks

In recent years, recommender systems based on heterogeneous information networks (HIN) have gained wide attention. In order to generate more attractive recommendations, weighted heterogeneous information network (WHIN) has been proposed, which attaches attribute values to links. The widely-used similarity measures for HIN may fail to capture the semantics of weighted meta-path. This makes designing a similarity measure specially for WHIN more necessary. In this paper, we propose a semantic path-based similarity measure called WgtSim, which is a generalization of PathSim presented by Sun et al. Furthermore, to demonstrate the capability of WgtSim in capturing semantics, we apply WgtSim to recommender system on WHIN to predict ratings given by users. The experiments on two real datasets show that the recommender system with WgtSim outperforms that with previous measures.